Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Probability in Statistics

Probability in Statistics

Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...

Bias

Bias

Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...

Binomial Probability Distribution

Binomial Probability Distribution

A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...

Probability Distributions

Probability Distributions

The probability of a random variable x is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson probability...

Poisson Probability Distribution

Poisson Probability Distribution

A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...

Probability Histograms

Probability Histograms

A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Stochastic heat engine using a single Brownian ellipsoid.

Physical review. E·2026

Same author

Role of extracellular vesicle-mediated neurodegeneration in substance use disorders.

Current opinion in physiology·2026

Same author

From global flocking to local clustering: Interplay between velocity alignment and visual perception of active particles.

The Journal of chemical physics·2026

Same author

Consideration of Pre- and Postanesthetic Evaluation During ECT in Cases of Isolated Eosinophilia in Endemic Regions.

The journal of ECT·2026

Same author

A Hypothesis-Generating Pharmacologic Profile for Sargramostim Treatment of Parkinson's Disease.

Cellular and molecular neurobiology·2026

Same author

Enhanced urine glucose sensing using two-dimensional TMDCs-based SPR biosensor.

Scientific reports·2026

Search research articles

Related Experiment Video

Updated: Jun 3, 2026

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

Work probability distribution and tossing a biased coin.

Arnab Saha¹, Jayanta K Bhattacharjee, Sagar Chakraborty

¹S N Bose National Centre for Basic Sciences, Saltlake, Kolkata, India. arnab@bose.res.in

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|March 17, 2011

Summary

Rare events in dissipated work, analogous to biased coin tosses, enable quantitative work probability distributions for Jarzynski equality. This provides a system-independent method for constructing these distributions.

More Related Videos

Optimization of Processing of Tiebangchui with Highland Barley Wine Based on the Box-Behnken Design Combined with the Entropy Method

Optimization of Processing of Tiebangchui with Highland Barley Wine Based on the Box-Behnken Design Combined with the Entropy Method

Published on: May 19, 2023

Related Experiment Videos

Last Updated: Jun 3, 2026

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

Optimization of Processing of Tiebangchui with Highland Barley Wine Based on the Box-Behnken Design Combined with the Entropy Method

Optimization of Processing of Tiebangchui with Highland Barley Wine Based on the Box-Behnken Design Combined with the Entropy Method

Published on: May 19, 2023

Area of Science:

Statistical mechanics
Non-equilibrium thermodynamics
Probability theory

Background:

The Jarzynski equality relates equilibrium free energy differences to non-equilibrium work measurements.
Dissipated work in non-equilibrium processes often exhibits rare events that are crucial for understanding system behavior.
Large deviation theory provides a framework for analyzing the probability of rare events.

Purpose of the Study:

To develop a quantitative work probability distribution for the Jarzynski equality.
To propose a system-independent method for constructing this distribution.
To explore the role of rare events in non-equilibrium statistical mechanics.

Main Methods:

Mapping rare events in dissipated work to large deviation phenomena.
Utilizing concepts from biased coin toss models.
Developing a theoretical framework for work probability distributions.

Main Results:

Demonstrated that rare events in dissipated work are sufficient to yield a quantitative work probability distribution for the Jarzynski equality.
Proposed a general recipe for constructing work probability distributions.
Established a connection between Jarzynski equality and large deviation theory.

Conclusions:

The developed framework offers a novel approach to understanding and modeling rare events in physical phenomena.
The proposed method for constructing work probability distributions is independent of specific system details.
This work is expected to be valuable for various applications in statistical physics and beyond.